Instructions to use prithivMLmods/Qwen3-4B-ft-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Qwen3-4B-ft-bf16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Qwen3-4B-ft-bf16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Qwen3-4B-ft-bf16")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Qwen3-4B-ft-bf16", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prithivMLmods/Qwen3-4B-ft-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Qwen3-4B-ft-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-4B-ft-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Qwen3-4B-ft-bf16

SGLang

How to use prithivMLmods/Qwen3-4B-ft-bf16 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Qwen3-4B-ft-bf16" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-4B-ft-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Qwen3-4B-ft-bf16" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Qwen3-4B-ft-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Qwen3-4B-ft-bf16 with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Qwen3-4B-ft-bf16
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-4B-ft-bf16

Qwen3-4B-ft-bf16 is a fine-tuned, moderately abliterated version of the Qwen3-4B model. Designed for enhanced context awareness and controlled expressiveness, this model balances precision with creativity across a wide range of tasks—from complex reasoning to natural dialogue, code generation, and multilingual understanding.

Key Features:

Improved Context Awareness
Retains and utilizes long-range contextual information effectively, making it ideal for long-form conversations, document understanding, and summarization tasks.
Moderate Abliteration
Introduces measured behavioral flexibility that enhances creativity and adaptability while maintaining reliability, alignment, and safety in outputs.
Dual Thinking Modes
Supports dynamic switching between thinking mode (for math, logic, and coding) and non-thinking mode (for general-purpose conversations), ensuring optimal task matching.
Multilingual Mastery
Excels in over 100 languages and dialects for translation, multilingual chat, and cross-lingual reasoning.
Tool-Ready Agent Capabilities
Designed to integrate with tool APIs and complex workflows, with consistent performance in both thinking and non-thinking contexts.

Quickstart with Hugging Face Transformers🤗

pip install transformers==4.51.3
pip install huggingface_hub[hf_xet]

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Qwen3-4B-ft-bf16"

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# Define input
prompt = "Describe how renewable energy impacts economic development."
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate output
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

# Parse thinking content
try:
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip()
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip()

print("thinking content:", thinking_content)
print("content:", content)

Best Practices

Sampling Settings:
- Thinking mode: temperature=0.6, top_p=0.95, top_k=20
- Non-thinking mode: temperature=0.7, top_p=0.8, top_k=20
Token Length:
- Standard: 32768 tokens
- Extended Reasoning Tasks: up to 38912 tokens
Prompt Design:
- Math Problems: Add "Please reason step by step, and put your final answer within \boxed{}."
- MCQs: Format answers as {"answer": "B"} for easy parsing.
- Multi-turn: Omit thinking logs in conversation history for cleaner context.